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Following the work of our previous study [7,11], we aimed to generalize our findings by applying the investigation to the delirium, sepsis, and AKI use cases at 1 community hospital and 1 specialized hospital.
J Med Internet Res 2024;26:e51409
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Acute Dialysis Quality Initiative recommends developing tools for predicting AKI, defined as KDIGO stage 2 or 3, rather than targeting all AKI stages. KDIGO stage 1 can be viewed more as a “risk of AKI.” Traditionally, AKI predictors or risk factors have been more strongly associated with higher-severity AKI [17,18]. This stronger association will likely result in more powerful and robust predictive machine learning algorithms.
JMIR Form Res 2023;7:e45979
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However, there are no gold standard scores for AKI; therefore, we compared the model only with other machine learning models for AKI events. The prediction performance of the individual models was measured as the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), specificity, and F1 score with a fixed sensitivity of 0.85, as considered in a previous study [21].
JMIR Med Inform 2021;9(11):e26426
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